{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "block_hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The rpy2.ipython extension is already loaded. To reload it, use:\n",
      "  %reload_ext rpy2.ipython\n"
     ]
    }
   ],
   "source": [
    "%load_ext rpy2.ipython\n",
    "%matplotlib inline\n",
    "\n",
    "from prophet import Prophet\n",
    "from matplotlib import pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import logging\n",
    "import warnings\n",
    "\n",
    "logging.getLogger('prophet').setLevel(logging.ERROR)\n",
    "logging.getLogger('cmdstanpy').setLevel(logging.ERROR)\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "block_hidden": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('https://raw.githubusercontent.com/facebook/prophet/main/examples/example_wp_log_peyton_manning.csv')\n",
    "df = df.loc[:180,]  # Limit to first six months\n",
    "m = Prophet()\n",
    "m.fit(df)\n",
    "future = m.make_future_dataframe(periods=60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "block_hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "R[write to console]: Loading required package: Rcpp\n",
      "\n",
      "R[write to console]: Loading required package: rlang\n",
      "\n",
      "R[write to console]: Disabling yearly seasonality. Run prophet with yearly.seasonality=TRUE to override this.\n",
      "\n",
      "R[write to console]: Disabling daily seasonality. Run prophet with daily.seasonality=TRUE to override this.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%%R\n",
    "library(prophet)\n",
    "df <- read.csv('https://raw.githubusercontent.com/facebook/prophet/main/examples/example_wp_log_peyton_manning.csv')\n",
    "df <- df[1:180,]\n",
    "m <- prophet(df)\n",
    "future <- make_future_dataframe(m, periods=60)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default Prophet will return uncertainty intervals for the forecast `yhat`. There are several important assumptions behind these uncertainty intervals.\n",
    "\n",
    "There are three sources of uncertainty in the forecast: uncertainty in the trend, uncertainty in the seasonality estimates, and additional observation noise.\n",
    "\n",
    "### Uncertainty in the trend\n",
    "The biggest source of uncertainty in the forecast is the potential for future trend changes. The time series we have seen already in this documentation show clear trend changes in the history. Prophet is able to detect and fit these, but what trend changes should we expect moving forward? It's impossible to know for sure, so we do the most reasonable thing we can, and we assume that the *future will see similar trend changes as the history*. In particular, we assume that the average frequency and magnitude of trend changes in the future will be the same as that which we observe in the history. We project these trend changes forward and by computing their distribution we obtain uncertainty intervals.\n",
    "\n",
    "One property of this way of measuring uncertainty is that allowing higher flexibility in the rate, by increasing `changepoint_prior_scale`, will increase the forecast uncertainty. This is because if we model more rate changes in the history then we will expect more in the future, and makes the uncertainty intervals a useful indicator of overfitting.\n",
    "\n",
    "The width of the uncertainty intervals (by default 80%) can be set using the parameter `interval_width`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "output_hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "R[write to console]: Disabling yearly seasonality. Run prophet with yearly.seasonality=TRUE to override this.\n",
      "\n",
      "R[write to console]: Disabling daily seasonality. Run prophet with daily.seasonality=TRUE to override this.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%%R\n",
    "m <- prophet(df, interval.width = 0.95)\n",
    "forecast <- predict(m, future)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "forecast = Prophet(interval_width=0.95).fit(df).predict(future)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, these intervals assume that the future will see the same frequency and magnitude of rate changes as the past. This assumption is probably not true, so you should not expect to get accurate coverage on these uncertainty intervals.\n",
    "\n",
    "### Uncertainty in seasonality\n",
    "By default Prophet will only return uncertainty in the trend and observation noise. To get uncertainty in seasonality, you must do full Bayesian sampling. This is done using the parameter `mcmc.samples` (which defaults to 0). We do this here for the first six months of the Peyton Manning data from the Quickstart:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "output_hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "R[write to console]: Disabling yearly seasonality. Run prophet with yearly.seasonality=TRUE to override this.\n",
      "\n",
      "R[write to console]: Disabling daily seasonality. Run prophet with daily.seasonality=TRUE to override this.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "SAMPLING FOR MODEL 'prophet' NOW (CHAIN 1).\n",
      "Chain 1: \n",
      "Chain 1: Gradient evaluation took 8.6e-05 seconds\n",
      "Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.86 seconds.\n",
      "Chain 1: Adjust your expectations accordingly!\n",
      "Chain 1: \n",
      "Chain 1: \n",
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      "Chain 1:  Elapsed Time: 2.10541 seconds (Warm-up)\n",
      "Chain 1:                2.37589 seconds (Sampling)\n",
      "Chain 1:                4.4813 seconds (Total)\n",
      "Chain 1: \n",
      "\n",
      "SAMPLING FOR MODEL 'prophet' NOW (CHAIN 2).\n",
      "Chain 2: \n",
      "Chain 2: Gradient evaluation took 7.4e-05 seconds\n",
      "Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.74 seconds.\n",
      "Chain 2: Adjust your expectations accordingly!\n",
      "Chain 2: \n",
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      "Chain 2: \n",
      "Chain 2:  Elapsed Time: 2.04288 seconds (Warm-up)\n",
      "Chain 2:                2.12795 seconds (Sampling)\n",
      "Chain 2:                4.17083 seconds (Total)\n",
      "Chain 2: \n",
      "\n",
      "SAMPLING FOR MODEL 'prophet' NOW (CHAIN 3).\n",
      "Chain 3: \n",
      "Chain 3: Gradient evaluation took 7.1e-05 seconds\n",
      "Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.71 seconds.\n",
      "Chain 3: Adjust your expectations accordingly!\n",
      "Chain 3: \n",
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      "Chain 3: \n",
      "Chain 3:  Elapsed Time: 2.24488 seconds (Warm-up)\n",
      "Chain 3:                2.5196 seconds (Sampling)\n",
      "Chain 3:                4.76448 seconds (Total)\n",
      "Chain 3: \n",
      "\n",
      "SAMPLING FOR MODEL 'prophet' NOW (CHAIN 4).\n",
      "Chain 4: \n",
      "Chain 4: Gradient evaluation took 6.5e-05 seconds\n",
      "Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.65 seconds.\n",
      "Chain 4: Adjust your expectations accordingly!\n",
      "Chain 4: \n",
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      "Chain 4: \n",
      "Chain 4:  Elapsed Time: 2.2803 seconds (Warm-up)\n",
      "Chain 4:                2.73488 seconds (Sampling)\n",
      "Chain 4:                5.01518 seconds (Total)\n",
      "Chain 4: \n"
     ]
    }
   ],
   "source": [
    "%%R\n",
    "m <- prophet(df, mcmc.samples = 300)\n",
    "forecast <- predict(m, future)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "m = Prophet(mcmc_samples=300)\n",
    "forecast = m.fit(df, show_progress=False).predict(future)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This replaces the typical MAP estimation with MCMC sampling, and can take much longer depending on how many observations there are - expect several minutes instead of several seconds. If you do full sampling, then you will see the uncertainty in seasonal components when you plot them:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "output_hidden": true
   },
   "outputs": [
    {
     "data": {
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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -w 9 -h 6 -u in\n",
    "prophet_plot_components(m, forecast)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = m.plot_components(forecast)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can access the raw posterior predictive samples in Python using the method `m.predictive_samples(future)`, or in R using the function `predictive_samples(m, future)`."
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Edit Metadata",
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
